臺灣博碩士論文加值系統

本論文提出以ROS軟體框架發展的三輪全方位移動多機器人的模糊稀疏增廣型訊息濾波器同步定位及地圖建立方法。以ROS為基礎的行動機器人實驗中，進行讀取雷射測距器的數據、慣性量測單元及三輪的里程訊息，用以進行機器人位置及方向的估測。本文提出一個創新的模糊稀疏擴展型訊息濾波同步定位及地圖(FSEIF SLAM) 方法，利用兩個簡單的模糊邏輯規則，用以調整機器人感測器的量測距離及角度，進而達成同步定位及地圖。接著定義三種里程計的誤差，用以進行評估所提出的FSEIF SLAM性能。四種比較性的模擬說明所提出的FSEIF SLAM相對於已存在SEIF SLAM及EKF SLAM，有其效能及優越性。最後提出一個合作式的模糊增廣型訊息濾波器同步定位及地圖方法，針對群組行動多說明機器人，結合K-means及Dijkstra演算法，在已知的地圖及相對關係中進行最佳路徑規劃，達成更有能效的地同步定位及地圖技術。比較性模擬結果說明比較性模擬結果闡述合作式的模糊增廣型訊息濾波器同步定位及地圖的效用性，及其適用於大範圍環境的可行性。

The thesis presented for a fuzzy sparse extended information filtering (FSEIF) method for simultaneous localization and mapping SLAM) of three-wheeled omnidirectional mobile multirobots with the robotic operating system (ROS) software framework. In the experimental ROS-based mobile robot, the ROS frame reads the readings of the used laser scanner, IMU sensor and odometry amounted on the three driving wheels, in order to estimate the robot’s position and orientation. A novel fuzzy SEIF SLAM (FSEIF SLAM) algorithm is proposed with two simple fuzzy logic rules to adjust the measured distance ranges and angles of the on-board ranging sensors. Three odometric errors are defined to evaluate the performance of the proposed FSEIF SLAM method. Through four comparative simulations, the proposed FSEIF SLAM method is shown effective and superior in comparison with existing SEIF SLAM and EKFSLAM. A cooperative Fuzzy SEIF SLAM approach is presented for a group of omnidirectional mobile multirobots, where the optimal path searching method is devised by incorporating with K-means and Dijkstra algorithm under the assumption of known map and correspondence conditions. The effectiveness and merits of the proposed cooperative FSEIF SLAM in a large-scale environment are well illustrated by carrying out comparative simulations made by multiple mobile robots.

中文摘要 i
Abstract ii
Contents iii
List of Figures vii
List of Tables x
Nomenclature xi
List of Acronyms xii
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Literature Survey 3
1.2.1 Related Work on SLAM for Mobile Robots 3
1.2.2 Related Work on SEIF for Mobile Robots 4
1.2.3 Related Work on ROS for Mobile Robots 5
1.3 Motivation and Objectives 7
1.4 Main Contributions 7
1.5 Thesis Organization 8
Chapter 2 Mechatronic Description of the Experimental ROS-Based Omnidirectional Mobile Robot 10
2.1 Introduction 10
2.2 System Structure and Mechatronic Design 11
2.3 Kinematic Model of the Omni-directional mobile Robot 14
2.4 Description of Key Components 16
2.4.1 Laser ranger finder – RPLIDAR A2M8 16
2.4.2 Raspberry pi 3 18
2.4.3 Arduino UNO 19
2.4.4 IMU-JY901 20
2.4.5 Dynamixel MX64-AR 21
2.4.6 Power convertor 22
2.4.7 Motion Controller of the Dynamixel 23
2.5 Building ROS Software Framework 24
2.5.1 Introduction to ROS 25
2.5.2 Robot Operating System Establishment 25
2.5.3 ROS Framework 27
2.5.4 Navigation stack of ROS 31
2.5.5 Hector SLAM 32
2.6 Experimental Results and Discussion 34
2.6.1 Laser Line Extraction Method and Corner Extraction Method 34
2.6.2 IMU sensor in ROS 36
2.6.3 Integrating Sensing Data in ROS 37
2.7 Chapter Summaries 38
Chapter 3 39
3.1 Introduction 39
3.2 SEIF SLAM 40
3.2.1 Introduction to SEIF SLAM 40
3.2.2 Sparsification Processing of the SEIF SLAM 40
3.2.3 Sparsification Analysis of the SEIF SLAM 42
3.3 FSEIF-SLAM 43
3.3.1 FSEIF SLAM algorithm process 45
3.4 Dead-Reckoning Method and Odometry Errors 47
3.4.1 Odometry errors 48
3.4.2 Error functions 49
3.5 Simulations and Discussion 50
3.5.1 Simulation 1: Extra noise 51
3.5.2 Simulation 2: SLAMs Comparison in term of RMSE 55
3.6 FSEIF SLAM Experiment 57
3.7 Chapter Summary 63
Chapter 4 Cooperative FSEIF SLAM of Omnidirectional Mobile Multirobots 65
4.1 Introduction 65
4.2 Optimal the Path Plan 66
4.2.1 K-means clustering 67
4.2.2 The shortest path – Dijkstra’s algorithm 67
4.2.3 Shortest Path Planning 69
4.3 Cooperative FSEIF SLAM for Multirobots 70
4.3.1 Sub-Map Merging Method 70
4.3.2 Cooperation Simulation 1 –Star Diagram 71
4.3.3 Cooperation Simulation 2 - Heart diagram 74
4.3.4 Cooperative Fuzzy SEIF SLAM 77
4.3.5 Discussion 79
4.4 Chapter Summaries 80
Chapter 5 Conclusions and Future Work 81
5.1 Conclusions 81
5.2 Future Work 82
References 84

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